{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1111\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1111\n"
     ]
    },
    {
     "data": {
      "image/png": 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u50WTrnUxkjwiyU1J/qrr3y93y09P8qkkn0lyTXeBf9k5TP9+P8nfDu2/MyZd65FIsirJLUk+3M2viP0Hzb6ttH23L8ltXV92dcsel+T6bv9dn+Sxk65zMQ7Rt7E/O6c2IIADwM9W1fcCzwJek2RDt+5tVXVG97NjciUekQeAF1TV04EzgI1JnsVguJG3VdV64IsMhiNZjg7VP4D/NLT/bp1ciUviYuCOofmVsv/gW/sGK2vfATy/68vB5x8uBf60239/2s0vV/P7BmN+dk5tQFTVvVV1czd9P4P/UU+dbFVLpwa+2s2e0P0U8AIGw44AXAX88ATKO2KH6d+KkWQtcB5wZTcfVsj+m9+3Y8gFDPYbLOP9t1SmNiCGJZkBzgQ+1S3akuSvk7xnuR4CwsOH8LcC9wHXA58FvlRVB7omcyzjUJzfv6o6uP/+W7f/3taN6LtcvR34z8BD3fzjWTn7b37fDlop+w4G/2D54yS7u+F8AL6zqu6FwT9Sge+YWHVHptU3GPOzc+oDIslJwAeB11XVV4DfBp7I4LTFvcBbJljeEamqb1bVGQyeMj8b+N5Ws6Nb1dKZ378kTwHeADwZOAt4HPD6CZa4aEl+ELivqnYPL240XXb77xB9gxWy74Y8u6q+HziXwSns50y6oCXU6tvYn51THRBJTmAQDu+rqg8BVNXnug+eh4DfZfDBuqxV1ZeAjzO41vKYbtgRaA9PsuwM9W9jd+qwquoB4PdYvvvv2cD5SfYB2xicWno7K2P/fUvfkrx3Be07AKrqnu73fcAfMujP55I8AaD7fd/kKly8Vt8W89k5tQHRnc99N3BHVb11aPkThpr9CMt0ZNcka5I8ppt+JHAOg+ssNzAYdgQGw5D878lUeGQO0b9PD/3xhcH53WW5/6rqDVW1tqpmGIwA8LGq+lFWwP47RN9evlL2HUCSRyVZfXAa+AEG/Rke/mdZ7r9D9W0xn53T/JWjzwZ+DLitO48N8PPAS7vb6wrYB/zUZMo7Yk8Arsrgi5WOA66tqg8nuR3YluRNwC0MQnI5OlT/PpZkDYPTMbcCr55kkT14PStj/7W8bwXtu+8E/nCQdRwPvL+q/k+SncC1SV4J/D3wkgnWuFiH6tvV4352OtSGJKlpak8xSZImy4CQJDUZEJKkJgNCktRkQEiSmgwIHbOSfLMb1XJPN+rsJUmO69bNJvmNw7x2JsnLjl610tHnba46ZiX5alWd1E1/B/B+4Maq+sURXvs84Oeq6gf7rVKaHI8gJB4ekmAzg8HMkuR5Q9+D8NyhMfRv6Z5SvRz4t92yn+mOKP4syc3dz7/pXvu8JB9P8oEkn07yvu5JZJKcleQvuqOXm5Ks7gY4/PUkO7tB1Zbrg6BaAab5SWrpqKqqu7pTTPNH8Pw54DVVdWM3eOTXGXxPwMNHEEm+HXhhVX09yXrgfwAHx+E/E/g+BuMy3Qg8O8lNwDXARVW1M8mjgX9m8P0RX66qs7rRUm9M8sdV9bd99l1qMSCkf6k1IuuNwFuTvA/4UFXNdQcBw04A3tkNZfBN4HuG1t1UVXMA3bAxM8CXgXuraidAN1IxSX4AeFqSg+M5nQysBwwIHXUGhNRJ8t0MPtzvY2jo9aq6PMlHgBcBn0xyTuPlPwN8Dng6g1O3Xx9a98DQ9DcZ/N2F9lDgAf5jVV13BF2RloTXICQGo88CvwO8s+bduZHkiVV1W1X9GrCLwXci3A+sHmp2MoMjgocYDDK5aoG3/DTwr5Kc1b3H6m6Y8OuAn+6GuifJ93QjckpHnUcQOpY9sjvlcwKD70C/Gnhro93rkjyfwb/+bwc+yuCb1g4k+Svg94HfAj6Y5CUMhvz+2uHeuKoeTHIR8JvdcOj/zGBI9CsZnIK6ubuYvZ9j/GsvNTne5ipJavIUkySpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJavp/HKTp/ZrmBpIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1111\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1111\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import cv2\n",
    "import argparse\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from utils import _gather_feature, _tranpose_and_gather_feature, flip_tensor,draw_hist\n",
    "import torch\n",
    "import torch.utils.data\n",
    "\n",
    "# from utils import load_model\n",
    "from image import transform_preds, get_affine_transform\n",
    "\n",
    "\n",
    "import glob\n",
    "# sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'lib'))\n",
    "sys.path.append(\"..\")\n",
    "from lib.nms.nms import soft_nms\n",
    "# from utils.post_process import ctdet_decode_csl_one\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def _softmax(x):\n",
    "    y = nn.Softmax(dim=2)(x)\n",
    "    return y\n",
    "\n",
    "def _nms(heat, kernel=3):\n",
    "    hmax = F.max_pool2d(heat, kernel, stride=1, padding=(kernel - 1) // 2)\n",
    "    keep = (hmax == heat).float()\n",
    "    return heat * keep\n",
    "\n",
    " \n",
    "    \n",
    "def _topk(scores, K=40):\n",
    "    batch, cat, height, width = scores.size()\n",
    "\n",
    "    topk_scores, topk_inds = torch.topk(scores.view(batch, cat, -1), K)\n",
    "#     print(\"#####################\",topk_scores)\n",
    "    \n",
    "    topk_inds = topk_inds % (height * width)\n",
    "    \n",
    "#     topk_ys = (topk_inds / width).int().float()#torch 1.5版本不再有这种除法\n",
    "    topk_ys = torch.floor_divide(topk_inds,width).int().float()\n",
    "#     result = torch.floor_divide(A, n)\n",
    "    \n",
    "    topk_xs = (topk_inds % width).int().float()\n",
    "\n",
    "    topk_score, topk_ind = torch.topk(topk_scores.view(batch, -1), K)\n",
    "#     print(\"---------------------------\",topk_score)\n",
    "    \n",
    "#     topk_clses = (topk_ind / K).int()\n",
    "    topk_clses = torch.floor_divide(topk_ind,K).int()\n",
    "    \n",
    "    topk_inds = _gather_feature(topk_inds.view(batch, -1, 1), topk_ind).view(batch, K)\n",
    "    topk_ys = _gather_feature(topk_ys.view(batch, -1, 1), topk_ind).view(batch, K)\n",
    "    topk_xs = _gather_feature(topk_xs.view(batch, -1, 1), topk_ind).view(batch, K)\n",
    "\n",
    "    return topk_score, topk_inds, topk_clses, topk_ys, topk_xs\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def ctdet_decode_dfl(hmap, tblr,reg_max, K=100):\n",
    "    project = torch.linspace(0, reg_max, reg_max + 1)\n",
    "    \n",
    "    batch, cat, height, width = hmap.shape\n",
    "    hmap = torch.sigmoid(hmap)\n",
    "\n",
    "    # if flip test\n",
    "#     if batch > 1:\n",
    "#         hmap = (hmap[0:1] + flip_tensor(hmap[1:2])) / 2\n",
    "#         w_h_ = (w_h_[0:1] + flip_tensor(w_h_[1:2])) / 2\n",
    "\n",
    "\n",
    "    batch = 1\n",
    "\n",
    "    hmap = _nms(hmap)  # perform nms on heatmaps\n",
    "#     print(hmap)\n",
    "    scores, inds, clses, ys, xs = _topk(hmap, K=K)\n",
    "#     print(\"sssssssssssssssssssssssss\",scores)\n",
    "    tblr = _tranpose_and_gather_feature(tblr, inds)\n",
    "#     print(tblr.shape)\n",
    "    t = tblr[:,:,:reg_max+1]\n",
    "    b = tblr[:,:,reg_max+1:2*reg_max+2]\n",
    "    l = tblr[:,:,2*reg_max+2:3*reg_max+3]\n",
    "    r = tblr[:,:,3*reg_max+3:4*reg_max+4]\n",
    "    t_ = Integral(t,project)\n",
    "    b_ = Integral(b,project)\n",
    "    l_ = Integral(l,project)\n",
    "    r_ = Integral(r,project)\n",
    "#     if np.random.random() < 0.1:\n",
    "#         print(t_,b_,l_,r_)\n",
    "    \n",
    "#     print(t_.shape)\n",
    "#     print(t_,b_,l_,r_)\n",
    "#     tblr = \n",
    "#     tblr = torch.tensor([t_,b_,l_,r_])\n",
    "#     regs = regs.view(batch, K, 2)\n",
    "#     print(\"regs\",regs)\n",
    "#     xs = xs.view(batch, K, 1) \n",
    "#     ys = ys.view(batch, K, 1)\n",
    "#     tblr = tblr.view(batch, K, 4)\n",
    "#     print(tblr.shape)\n",
    "    clses = clses.view(batch, K, 1).float()\n",
    "    scores = scores.view(batch, K, 1)\n",
    "#     print(\"gggggggggggggggggggggggggggggg\",scores)\n",
    "\n",
    "    #神坑！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！(...)三个点\n",
    "    bboxes = torch.cat([xs - l_,\n",
    "                        ys - t_,\n",
    "                        xs + r_,\n",
    "                        ys + b_], dim=2)\n",
    "#     print(bboxes)\n",
    "    detections = torch.cat([bboxes, scores, clses], dim=2)\n",
    "#     print(\"000000000000000000000000000000000000000\",detections)\n",
    "    return detections\n",
    "\n",
    "def Integral(x,project):\n",
    "    weight = x\n",
    "#     weight = F.softmax(x, dim=2)\n",
    "    pred_ = weight*project.type_as(weight)\n",
    "    pred_final = torch.unsqueeze(torch.sum(pred_,dim=2),dim=2)\n",
    "    \n",
    "    return pred_final\n",
    "  \n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "def ctdet_decode_csl_one(hmap, tblr, reg_max,K=100):\n",
    "    project = torch.linspace(0, reg_max-1, reg_max )\n",
    "    batch, cat, height, width = hmap.shape\n",
    "    hmap = torch.sigmoid(hmap)\n",
    "\n",
    "    # if flip test\n",
    "#     if batch > 1:\n",
    "#         hmap = (hmap[0:1] + flip_tensor(hmap[1:2])) / 2\n",
    "#         w_h_ = (w_h_[0:1] + flip_tensor(w_h_[1:2])) / 2\n",
    "\n",
    "\n",
    "    batch = 1\n",
    "\n",
    "    hmap = _nms(hmap)  # perform nms on heatmaps\n",
    "#     print(hmap)\n",
    "    scores, inds, clses, ys, xs = _topk(hmap, K=K)\n",
    "    \n",
    "#     print(\"sssssssssssssssssssssssss\",scores)\n",
    "    tblr = _tranpose_and_gather_feature(tblr, inds)\n",
    "#     print(tblr.shape)\n",
    "    t = tblr[:,:,:reg_max]\n",
    "    b = tblr[:,:,reg_max:2*reg_max]\n",
    "    l = tblr[:,:,2*reg_max:3*reg_max]\n",
    "    r = tblr[:,:,3*reg_max:4*reg_max]\n",
    "#     regs = regs.view(batch, K, 2)\n",
    "#     print(\"regs\",regs)\n",
    "    xs = xs.view(batch, K, 1) \n",
    "    ys = ys.view(batch, K, 1)\n",
    "#------------------------------------------------------------------------------------------    \n",
    "    t = _softmax(t.view(batch, K, reg_max))\n",
    "    \n",
    "    ##############################################################################\n",
    "#     画图用\n",
    "\n",
    " ##############################################################################\n",
    "#     print(t)\n",
    "#     print(torch.max(t,dim=2))\n",
    "    t_s =torch.max(t,dim=2)[0].view(batch, K, 1)\n",
    "#     print(t)\n",
    "    t = t.float()\n",
    "    \n",
    "    b = _softmax(b.view(batch, K, reg_max))\n",
    "\n",
    "    b_s =torch.max(b,dim=2)[0].view(batch, K, 1)\n",
    "    b = b.float()\n",
    "    \n",
    "    l = _softmax(l.view(batch, K, reg_max))\n",
    "\n",
    "    l_s =torch.max(l,dim=2)[0].view(batch, K, 1)\n",
    "    l = l.float()\n",
    "\n",
    "    r = _softmax(r.view(batch, K, reg_max))\n",
    "\n",
    "    r_s =torch.max(r,dim=2)[0].view(batch, K, 1)\n",
    "    r = r.float()\n",
    "    \n",
    "###############################################################################\n",
    "    #画图用\n",
    "    t_list = t[0,0,:].cpu().data.numpy()\n",
    "#     print(t[0,0,:].cpu().data.numpy())\n",
    "    draw_hist(t_list, '', 'Distance', 'Probability', 0, reg_max, 0.0, 1.0,\"t\")  # 直方图展示\n",
    "    \n",
    "    b_list = b[0,0,:].cpu().data.numpy()\n",
    "#     print(b[0,0,:].cpu().data.numpy())\n",
    "    draw_hist(b_list, '', 'Distance', 'Probability', 0, reg_max, 0.0, 1.0,\"b\")  # 直方图展示\n",
    "    \n",
    "    l_list = l[0,0,:].cpu().data.numpy()\n",
    "#     print(l[0,0,:].cpu().data.numpy())\n",
    "    draw_hist(l_list, '', 'Distance', 'Probability', 0, reg_max, 0.0, 1.0,\"l\")  # 直方图展示\n",
    "    \n",
    "    r_list = r[0,0,:].cpu().data.numpy()\n",
    "#     print(r[0,0,:].cpu().data.numpy())\n",
    "    draw_hist(r_list, '', 'Distance', 'Probability', 0, reg_max, 0.0, 1.0,\"r\")  # 直方图展示\n",
    " ###############################################################################\n",
    "    t = Integral(t,project)\n",
    "    b = Integral(b,project)\n",
    "    l = Integral(l,project)\n",
    "    r = Integral(r,project)\n",
    "#     t = t*2\n",
    "#     b = b*2\n",
    "#     l = l*2\n",
    "#     r = r*2\n",
    "    clses = clses.view(batch, K, 1).float()\n",
    "    scores = scores.view(batch, K, 1)\n",
    "    \n",
    "#     scores = scores*reg_score\n",
    "#     print(\"gggggggggggggggggggggggggggggg\",scores)\n",
    "\n",
    "    #神坑！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！(...)三个点\n",
    "    bboxes = torch.cat([xs - l,\n",
    "                        ys - t,\n",
    "                        xs + r,\n",
    "                        ys + b], dim=2)\n",
    "#     print(bboxes)\n",
    "    detections = torch.cat([bboxes, scores, clses], dim=2)\n",
    "#     print(detections.shape)\n",
    "#     print(\"000000000000000000000000000000000000000\",detections)\n",
    "    return detections\n",
    "# 参数依次为list,抬头,X轴标签,Y轴标签,XY轴的范围\n",
    "def draw_hist(myList, Title, Xlabel, Ylabel, Xmin, Xmax, Ymin, Ymax, name):\n",
    "    #     plt.hist(myList, 50) # bins = 50，顺便可以控制bin宽度\n",
    "    x = tuple([i for i in range(Xmax)])\n",
    "    max_ = max(myList)\n",
    "    print(1111)\n",
    "#     index_max = mp.argmax(mylist)\n",
    "    myList_ = list(myList)\n",
    "    index_max = myList_.index(max(myList_))\n",
    "    Xmin = max(0,index_max-15)\n",
    "    Xmax = min(index_max+15,100)\n",
    "    Ymax = min(max_+0.3,1)\n",
    "    plt.bar(x, myList)\n",
    "    plt.xlim(Xmin, Xmax)\n",
    "    plt.ylim(Ymin, Ymax)\n",
    "    plt.xlabel(Xlabel)  # 横轴名\n",
    "    plt.ylabel(Ylabel)  # 纵轴名\n",
    "\n",
    "    plt.title(Title)\n",
    "    plt.savefig(\"./distribution/{}_init_{}.pdf\".format(img_name,name))\n",
    "#     plt.clf()  # 清楚画布\n",
    "#     plt.imshow()\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n",
    "# COCO_NAMES = ['__background__', \"windmill\",\"vehicle\",\"trainstation\",\"tenniscourt\",\"storagetank\",\"stadium\",\"ship\",\"harbor\",\"groundtrackfield\",\n",
    "#                \"golffield\",\"Expressway-toll-station\",\"Expressway-Service-area\",\"dam\",\"chimney\",\"bridge\",\"overpass\",\"basketballcourt\",\n",
    "#               \"baseballfield\",\"airport\",\"airplane\"]#dior\n",
    "COCO_NAMES = ['__background__',\"airplane\",\"baseball diamond\",\"basketball court\",\n",
    "              \"bridge\",\"crossroad\",\"ground track field\",\"harbor\",\n",
    "              \"parking lot\",\"ship\",\"storage tank\",\"T junction\",\"tennis court\",\"vehicle\"]\n",
    "\n",
    "COCO_COLORS = sns.color_palette('Paired', 20)\n",
    "COCO_IDS = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13,\n",
    "            14, 15, 16, 17, 18, 19, 20, 21, 22, 23,\n",
    "            24, 25, 27, 28, 31, 32, 33, 34, 35, 36,\n",
    "            37, 38, 39, 40, 41, 42, 43, 44, 46, 47,\n",
    "            48, 49, 50, 51, 52, 53, 54, 55, 56, 57,\n",
    "            58, 59, 60, 61, 62, 63, 64, 65, 67, 70,\n",
    "            72, 73, 74, 75, 76, 77, 78, 79, 80, 81,\n",
    "            82, 84, 85, 86, 87, 88, 89, 90]\n",
    "\n",
    "COCO_MEAN = [0.38208183, 0.39433967, 0.36092791]\n",
    "COCO_STD = [0.15639887, 0.14444648, 0.14092942]\n",
    "COCO_EIGEN_VALUES = [0.2141788, 0.01817699, 0.00341571]\n",
    "COCO_EIGEN_VECTORS = [[-0.58752847, -0.69563484, 0.41340352],\n",
    "                      [-0.5832747, 0.00994535, -0.81221408],\n",
    "                      [-0.56089297, 0.71832671, 0.41158938]]\n",
    "\n",
    "max_per_image = 100\n",
    "num_classes =13\n",
    "img_size = 800\n",
    "img_path = '../../2nd_paper_glfpn/hrrsd/JPEGImages/21233.jpg'\n",
    "# weight_dir = \"../weights/hrrsd/dlanet34_KL_cls_mean0.2_std0.05_L0.3_16(EGK_0.7_only_train_800_30epoch)+cls_reg/best_map.pth\"#init\n",
    "weight_dir = \"../weights/hrrsd/dlanet34_cls_sml0.1_std0.05_L0.3_16(EGK_0.7_only_train_800_30epoch)+cls_reg/best_map.pth\"#交叉熵只有正样本\n",
    "# weight_dir = \"../weights/hrrsd/dlanet34_cls_L0.3_16(EGK_0.7_only_train_800_30epoch)+cls_reg/best_ap50.pth\"#KL\n",
    "# weight_dir = \"../weights/hrrsd/dlanet34_cls_sml0.1_std0.01_L0.3_16(EGK_0.7_only_train_800_30epoch)+cls_reg/best_map.pth\"\n",
    "# weight_dir = \"../weights/hrrsd/dlanet34_cls_mean0.1_std0.01_L0.3_16(EGK_0.7_only_train_800_30epoch)+cls_reg/best_map.pth\"#mean_var\n",
    "method = \"dfl_csl\"\n",
    "\n",
    "img_name = os.path.basename(img_path).split(\".\")[0]\n",
    "\n",
    "\n",
    "device = torch.device('cuda')\n",
    "if method == \"dfl_csl\":\n",
    "    from nets.dlanet_vis import DlaNet\n",
    "    model = DlaNet(num_classes=num_classes,head_conv=256,pretrained=True)#试一下256效果怎么样\n",
    "\n",
    "model.load_state_dict(torch.load(weight_dir ), strict=False)\n",
    "model = model.to(device)\n",
    "model.eval()\n",
    "    \n",
    "    \n",
    "image = cv2.imread(img_path)\n",
    "orig_image = image\n",
    "height, width = image.shape[0:2]\n",
    "padding = 31\n",
    "imgs = {}\n",
    "new_width = width\n",
    "new_height = height\n",
    "\n",
    "if img_size > 0:\n",
    "    img_height, img_width = img_size, img_size\n",
    "    center = np.array([new_width / 2., new_height / 2.], dtype=np.float32)\n",
    "    scaled_size = max(height, width) * 1.0\n",
    "    scaled_size = np.array([scaled_size, scaled_size], dtype=np.float32)\n",
    "else:\n",
    "    img_height = (new_height | padding) + 1\n",
    "    img_width = (new_width | padding) + 1\n",
    "    center = np.array([new_width // 2, new_height // 2], dtype=np.float32)\n",
    "    scaled_size = np.array([img_width, img_height], dtype=np.float32)\n",
    "\n",
    "img = cv2.resize(image, (new_width, new_height))\n",
    "trans_img = get_affine_transform(center, scaled_size, 0, [img_width, img_height])\n",
    "img = cv2.warpAffine(img, trans_img, (img_width, img_height))\n",
    "\n",
    "img = img.astype(np.float32) / 255.\n",
    "img -= np.array(COCO_MEAN , dtype=np.float32)[None, None, :]\n",
    "img /= np.array(COCO_STD , dtype=np.float32)[None, None, :]\n",
    "img = img.transpose(2, 0, 1)[None, :, :, :]  # from [H, W, C] to [1, C, H, W]\n",
    "\n",
    "\n",
    "imgs = {'image': torch.from_numpy(img).float(),\n",
    "                           'center': np.array(center),\n",
    "                           'scale': np.array(scaled_size),\n",
    "                           'fmap_h': np.array(img_height // 4),\n",
    "                           'fmap_w': np.array(img_width // 4)}\n",
    "\n",
    "\n",
    "\n",
    "with torch.no_grad():\n",
    "    detections = []\n",
    "    imgs['image'] = imgs['image'].to(device)\n",
    "    output = model(imgs['image'])[-1]\n",
    "    \n",
    "    if method == \"dfl_csl\":\n",
    "        output = output[:-1]\n",
    "        dets = ctdet_decode_csl_one(*output,reg_max = 100)\n",
    "        dets = dets.detach().cpu().numpy().reshape(1, -1, dets.shape[2])[0]\n",
    "        top_preds = {}\n",
    "        dets[:, :2] = transform_preds(dets[:, 0:2],\n",
    "                                              imgs['center'],\n",
    "                                              imgs['scale'],\n",
    "                                              (imgs['fmap_w'], imgs['fmap_h']))\n",
    "        dets[:, 2:4] = transform_preds(dets[:, 2:4],\n",
    "                                               imgs['center'],\n",
    "                                               imgs['scale'],\n",
    "                                               (imgs['fmap_w'], imgs['fmap_h']))\n",
    "        cls = dets[:, -1]\n",
    "        for j in range(num_classes):# if cfg.dataset == 'ssdd_coco',为2\n",
    "            inds = (cls == j)\n",
    "            top_preds[j + 1] = dets[inds, :5].astype(np.float32)\n",
    "            top_preds[j + 1][:, :4] /= 1\n",
    "\n",
    "        detections.append(top_preds)\n",
    "\n",
    "    bbox_and_scores = {}\n",
    "    for j in range(1, num_classes+1):# if cfg.dataset == 'ssdd_coco',为2\n",
    "            bbox_and_scores[j] = np.concatenate([d[j] for d in detections], axis=0)\n",
    "            soft_nms(bbox_and_scores[j], Nt=0.5, method=2)\n",
    "    scores = np.hstack([bbox_and_scores[j][:, 4] for j in range(1, num_classes+1)])\n",
    "\n",
    "    if len(scores) > max_per_image:\n",
    "        kth = len(scores) - max_per_image\n",
    "        thresh = np.partition(scores, kth)[kth]\n",
    "        for j in range(1, num_classes+1):\n",
    "            keep_inds = (bbox_and_scores[j][:, 4] >= thresh)\n",
    "            bbox_and_scores[j] = bbox_and_scores[j][keep_inds]\n",
    "\n",
    "    img = orig_image\n",
    "    colors = COCO_COLORS \n",
    "    names = COCO_NAMES \n",
    "\n",
    "    #             print(bbox_and_scores)\n",
    "    for category,lab in enumerate(bbox_and_scores):\n",
    "    #                 print(category)\n",
    "    #                 print(COCO_COLORS[category])\n",
    "        for boxes in bbox_and_scores[lab]:\n",
    "            x1, y1, x2, y2, score = boxes\n",
    "    #                     print(score)\n",
    "            if score>0.3:\n",
    "#                 print(\"1111111111\")\n",
    "    #                         color = [i*230 for i in list(COCO_COLORS[category])]\n",
    "                color = [0,0,255]\n",
    "                cv2.rectangle(img,(x1, y1),(x2, y2),color,20)\n",
    "#                 cv2.imshow('test.jpg',img)\n",
    "                img = img[:,:,::-1] \t# transform image to rgb\n",
    "    plt.imshow(img)\n",
    "    plt.show()\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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